molecular modelling and computer-aided drug design: the

Hacettepe University Journal of the Faculty of PharmacyVolume 40 / Number 1 / January 2020 / pp. 34-47

Molecular Modelling and Computer-Aided Drug Design: The Skill Set Every Scientist in Drug Research Needs and Can Easily GetMoleküler Modelleme ve Bilgisayar Destekli İlaç Tasarımı: İlaç Araştırmalarında Yer Alan Her Bilim İnsanının İhtiyaç Duyduğu ve Kolayca Edinebileceği Beceri Seti

Review Article

ABSTRACTThe overwhelming advances in molecular modelling witnessed for a couple of decades go hand in hand with the booming computer and information technolo-gies. Computer-aided drug design (CADD) is probably the most important field of molecular modelling given the time scale and cost for turning a chemical entity into an approved drug. In this review we provide a brief definition of molecu-lar modelling and CADD with historical corner stones. In this review methods, tools, and applications of molecular modelling in different stages of CADD were focused on by referring to a number of success stories. Useful databases and non-commercial software for different purposes are also introduced. The review aims to provide a glimpse of these methods for scientists taking part in any field of drug research and to show that everyone can and should make the best of these methods with a vast amount of available free tools and documentation.

Keywords: molecular modelling, computer-aided drug design, virtual screening,

molecular docking, pharmacophore modelling, shape similarity, molecular dy-

namics simulations, ADMET prediction

ÖZETMoleküler modellemede son birkaç on yıldır tanıklık ettiğimiz baş döndürücü gelişmeler ilerleyen bilgisayar ve enformasyon teknolojileri ile birlikte gerçek-leşmektedir. Bilgisayar destekli ilaç tasarımı (BDİT), bir kimyasalın ilaca dönüş-türülmesi için gerekli zaman ve masraflar göz önüne alındığında, muhtemelen moleküler modellemenin en önemli alanıdır. Mevcut derlemede BDİT’nın farklı aşamalarındaki moleküler modelleme yöntemleri, araçları ve uygulamaları-na, bazı başarı hikayelerine de atıfta bulunularak odaklanılmıştır. Ayrıca, farklı amaçlar için kullanılan faydalı veri tabanları ve ticari olmayan yazılımlar tanıtıl-mıştır. Derleme, bu yöntemlerle ilgili, ilaç araştırmalarının herhangi bir alanında görev alan bilim insanlarına fikir vermeyi ve herkesin bu yöntemlerden, mevcut çok sayıda ücretsiz yazılım ve dokümantasyon ile en iyi şekilde faydalanabilece-ğini göstermeyi amaçlamaktadır.

Anahtar Kelimeler: moleküler modelleme; bilgisayar destekli ilaç tasarımı; sanal aktivite tarama; moleküler kenetleme; farmakofor modelleme; şekil benzerliği; moleküler dinamik simülasyonları; ADMET tahmini

Suat SARI*

Hacettepe University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Ankara, Turkey

Corresponding author: Suat Sari Hacettepe University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, 06100 Sihhiye, Ankara,Turkey Tel.: + 90 312 305 18 72 Fax: + 90 312 305 32 72 E-mail: [email protected] , [email protected]

Received date : 18.04.2020 Accepted date : 26.05.2020

1. Introduction

The overwhelming advances in information technol-ogy observed in the past couple of decades, among many other things, made it possible to understand and predict molecules at every level of complexity by simply using our PCs or even our mobile de-vices. Thanks to multicore processors and high-end graphics available to almost everyone, molecular modelling is no longer the expertise of only those with highly technical computing skillset. Diversity of the methods in molecular modelling enabled this approach to reach to various fields in life sciences. Any researcher who crosses molecules’ path can get to know of many things in silico before or after an experiment (1, 2). Is my molecule water soluble enough to run an assay or how can I make it water soluble? Can this compound pass blood-brain bar-rier? Does it contain any functional groups that can react nonspecifically with the assay medium? Can it inhibit CYP3A4? Could this peptide assume an α-helix conformation? Which mutations could pos-sibly affect the function of my protein? Could this antagonist have triggered a desensitized state for my receptor? Questions like these can find more accu-rate answers in shorter time with the hands of the inquirers themselves thanks to molecular modelling.

Of course, molecular modelling has a special place in drug research. Given the time span and costs for a chemical entity to be labelled as “drug”, the ques-tions such as “which compound”, “which targets”, and “which off-targets” definitely need to be ad-dressed at the very beginning. At this point, molecu-lar modelling is put in use to work out what we call

“computer-aided drug design” (CADD) to save time and money, to dodge pit falls and dead ends, and to detect blind spots (3).

In this review, molecular modelling and virtual screening is defined and a historical background is provided with drug discovery perspective. Steps of CADD are presented with state-of-the-art methodol-ogies, applications, and success stories. The review also compiles resources for the most common tools of CADD.

2. Molecular Modelling and Its Historical Background

Molecular modelling is all the computational meth-ods used to predict molecular structure and behavior.

From material science to structural biology, molecu-lar modelling methodologies are used in many fields to understand systems made up of a wide range of complexity from small molecules to biological mac-romolecules such as proteins, receptors, and nu-cleotide chains. These systems can be modelled by treating atoms as particles with charges and potential energy using forcefields (molecular mechanics) or by applying wave function at atomic and subatomic scales (quantum mechanics) (4). From drawn repre-sentations of chemical structures to millisecond-long simulations of biological systems, molecular model-ling has undergone a massive progress through his-tory (5). Following are some of the milestones of molecular modelling:

The term chemical structure was introduced between 1858 and 1861 by identification of va-lence rules in organic chemistry and representing bonds as lines in molecules with carbon chains.

In 1865, August Wilhelm Hofmann for the first time used physical models in his organic chemis-try lecture, in which organic compounds such as methane and chloroform were represented with croquet balls joined by sticks.

Hofmann also established today’s commonly ac-cepted color scheme for atoms: black for carbon, white for hydrogen, blue for nitrogen and green for chlorine.

Crum-Brown in 1865 and Sir Edward Frankland and B. F. Duppa in 1867 were the scientists to use 2D drawn structures in ball and stick models for the first time.

That carbon compounds have tetrahedral geom-etry was first suggested by E. Paterno (1869), Jacobus Henricus van’t Hoff, and Joseph Achille LeBel (1874), which is considered as the emer-gence of three-dimensional molecular structure elaboration.

In 1898, van’t Hoff suggested that each carbon-carbon bond had a favored conformation about a torsional angle, setting the foundations of what we refer to as the global minimum energy confor-mations today.

Urey and Bradley introduced a formulation which included quadratic Hooke’s Law potential equa-tions to describe harmonic vibrations in simple molecules and found the Morse potential to give the best fit to empirical data for bond stretching

Hacettepe University Journal of the Faculty of Pharmacy

ISSN: 2458 - 880635

in 1931. This was a breakthrough in forcefield concept.

The first examples of molecular mechanics came in 1946 with Hill’s force field to treat the repulsive and attractive nonbonding terms, and quadratic terms for bond stretch and angle bend; Dostrovsky, Hughes and Ingold’s study on the repulsive and attractive nonbonding terms; and Westheimer and Mayer’s analyses of the confor-mations of hindered biphenyls. These methods let molecular modelling spread out of physical chemistry society.

The importance of 3D aspects of molecules in understanding structure, stability, conformation, and reactivity was appreciated with the emer-gence of conformation analysis led by the study of Barton on the conformations of steroids in 1950.

The most famous physical molecular model of all time, the structure of DNA double strand, was elucidated by Watson and Crick in 1953.

The first molecular dynamics (MD) calculations study was reported in the same year under the name Equation of State Calculations by Fast Computing Machines, which featured simulated annealing and aided the groundwork for Monte Carlo simulations.

Potentials of nonbonding interactions in organic structure modelling were first applied by Kitaig-orodsky in 1960.

The first use of computer for empirical force field calculations was reported in 1961 by Hendrick-son.

In 1965, Wiberg developed a steepest descent algorithm for geometry optimization to address conformational analysis.

Scientists from Oak Ridge National Laboratory in the US designed a molecular drawing program called ORTEP (Oak Ridge National Laboratory) in 1965. The US government also funded the first computer network in 1969, which is accepted as the ancestor of internet.

In 1971, Lee and Richards described the molecu-lar surface in protein structure context and eluci-dated an algorithm to derive it.

In 1974, computer modelling of oligosaccharides starting from crystal structure was reported for the first time. In the same year, force field cal-culations of synthetic macromolecules started to appear.

At the beginning of 1980s, with the booming per-sonal computer (PC) industry, molecular model-ling became accessible from PCs and the use of the graphical user interface (GUI) started. This marks the advent of personal molecular model-ling for the average chemist.

The World Wide Web kickstarted in 1993, which probably marks the beginning of web servers for molecular modelling.

3. Computer Aided Drug Design (CADD)

Drug design and discovery is highly complex and ex-pensive process that requires contribution of a wide range of disciplines. The general estimation is that it takes more than 10 years and a billion US dollars for a chemical entity to be used in the clinic. Although in silico methods are usually adopted in the early-to-mid-stage drug discovery studies, the selection of candidates passed from these stages to preclinical and clinical phases greatly affects the attrition rates. Therefore, the use of in silico methods in drug dis-covery has increased reasonably for the past couple

Figure 1. Steps of drug discovery and applications of CADD.


Volume 40 / Number 1 / January 2020 / pp. 34-47 Sari et al.36

of decades (Figure 1). CADD applications mainly assist experimental studies in decision making by following the major drug discovery stages, such as target validation, hit identification, lead generation, and optimization (3, 6).

3.1. Target Identification and Validation

Drug development process usually starts with identi-fication of a druggable target relevant to the disease of interest. Introduction of such targets prompts efforts to find potential compounds to modulate the target pathway and finally to elicit a phenotypic response (7). In silico approaches are usually helpful to un-derstand the structure, topology, and ligand binding sites of the target, as well as their key residues and ligand binding interactions. In silico methods such as homology modelling, molecular docking, and MD simulations have become paramount to assist in vitro studies for target validation such as protein structure elucidation (e.g. X ray diffraction), alanine scanning, site-directed mutagenesis, and radioligand binding (8-10).

Chemical and biological databases play crucial role in bringing the wet lab and computers together. Cur-rently, there is a vast number of databases which ar-chive and process countless sorts of data belonging to millions of molecules ranging from small mole-cules to proteins, genes, and nucleotide chains (Table 1) (11).

3.2. Hit Identification

Once a target is validated for a certain disorder, cam-paigns are run for identification of hit matter, i.e. chemical entities that display an intended activity against the target, by both pharma companies and academia. Just as the robotics technology made it possible to randomly screen biological activity of thousands of compounds in vitro at the same time (high-throughput screening), the computer technolo-gy did the same for screening millions of compounds in silico. With the so-called high-throughput virtual screening (or briefly virtual screening), biological screening of small compound sets rationally selected from huge libraries became more common. Virtual screening features a number of filters to narrow down the libraries step by step and to eventually sug-gest potential hits for in vitro tests (12). As the filters go from rough to exhaustive, the computational bur-den and time required increases dramatically, which, however is balanced with shrinking library through-

out the steps. Evaluation of druglike chemical space and PAINS (Pan-assay Interference Compounds) is an example for rough filters, which is commonly applied at the beginning of virtual screening cam-paigns (13, 14). This step is crucial for eliminat-ing entities with potentially poor pharmacokinetics and those with non-specific reactive functionalities, since such compounds account for a good amount of failed clinical candidates (15). This step is usually followed by a ligand- and/or structure-based virtual screening (16).

3.3. Lead Generation and Optimization

Finding hit compounds with ability to modulate an isolated target is the tip of the iceberg in drug dis-covery issues. When evaluated thoroughly (plotting dose-response curves, profiling for related targets, testing against cell cultures, in vivo and toxicity profiling, cytochrome P450 and efflux pump inter-actions, pharmacokinetics etc.) these hits will most likely fail at a certain point. This is where medici-nal chemists take the stage to tailor these hits to fit specific requirements (17). In silico drug discovery methods enable medicinal chemists to create librar-ies of countless virtual compounds envisaged accord-ingly. The so-called scaffold-hopping methodology utilizes both ligand- and structure-based approaches to model structurally relevant molecules as synthetic candidates (18). Establishing structure-activity rela-tionships (SARs) at this stage is crucial for creating virtual libraries. Quantitative SARs (QSAR) is an inevitable computation tool for decision making re-garding SARs (19). The synthesized compounds are then subjected to a set of in vitro and in vivo tests to obtain leads.

Lead optimization stage mainly focuses on pharma-cokinetic and toxicity issues, i.e. ADME+T (Absorp-tion, Distribution, Metabolism, Excretion + Toxic-ity), therefore in silico methods are less likely to be included at this level. However molecular modelling offers a wide range of tools to assist decision making for various scenarios. There are many web servers and free tools to evaluate ADME+T profile of com-pounds through fast in silico predictions, as well as more sophisticated, specific, and exhaustive models. Site of metabolism, hERG channel affinity, blood-brain barrier permeability, human serum albumin binding, CYP and P-glycoprotein affinity are among the properties that scientists can predict without a high level of expertise in computational chemistry


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and bioinformatics (20-23).

4. Approaches in CADD

Rapid development in information technologies brought about a vast amount of data, which in-creased our ability to create highly predictive models. In CADD, data is related to either small molecules or their potential targets, i.e. biological macromol-ecules. Virtual screening approaches in CADD can be classified as ligand-based and structure/receptor-based methods according to the type of employed data. Ligand-based methods use models built exclu-

sively from ligand data, while structure-based or re-ceptor-based approaches benefit from structural data of target macromolecules. Virtual screening methods, in most cases, incorporate both approaches, which are referred to as hybrid methods. This, of course, has much to do with the extend of the data available (16).

4.1. Ligand-Based Methods

The general consideration in CADD is that similar li-gands are supposed to exert similar biological effects, which lies beneath the rationale of ligand-based

Table 1. Examples of useful chemical and biological databases Database 1 Source Description

ZINC https://zinc.docking.org Indexes more than 230M commercial compounds in ready-to-dock format with diverse data including predicted and biological properties and vendor information.

ChEMBL https://www.ebi.ac.uk/chembl/ Archives 2M compounds with detailed experimental data, target, assay, and literature information.

DrugBank www.drugbank.ca Includes 13,551 drug entries (approved and experimental) with detailed information including drug targets.

PubChem https://pubchem.ncbi.nlm.nih.gov Archives 103M compounds with relevant chemical, bioactivity, and literature information.

ChemSpider www.chemspider.com Provides 81M compounds with 278 data sources including supplier information.

DUD-E http://dude.docking.org Provides 22K active compounds with target information and 50 decoys for each active compound for virtual screening enrichment studies.

BindingDB www.bindingdb.org/bind Stores measured affinities of 820K small molecules to 7K protein targets.

Protein data banks

www.wwpdb.orgwww.pdbe.orgwww.rcsb.orgwww.bmrb.wisc.eduwww.pdbj.org

Store three-dimensional structural data of biological macromolecules. Currently, more than 160K structurers are available.

UniProt www.uniprot.org Provides sequence and functional information for some 177M proteins.

NCBI www.ncbi.nlm.nih.gov Along with literature (PubMed) and many other databases, NCBI is one of the largest protein, DNA, RNA, genome, and gene databases.

SCOP http://scop.mrc-lmb.cam.ac.uk Provides a detailed and comprehensive description of structural and evolutionary relationships between 532K proteins with known structure.

BioGRID https://thebiogrid.org Biological database of protein-protein interactions, genetic interactions, chemical interactions, and post-translational modifications.

PROSITE https://prosite.expasy.org Consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them.

1in alphabetical order



CADD (24). Similarity can be measured in diverse ways, i.e. molecular descriptors, which incorporate molecular properties. According to the complexity and organization of the data, ligand-based methods are classified as 1D-, 2D- or 3D-methods (25). De-scriptors for 1D-methods include physicochemical properties of molecules without structural data, such as molecular weight, H bond acceptor count, and po-lar surface area. The idea of 1D-methods suggests that compounds belong to a specific group, such as orally available small-molecule drugs, possess similar set of physicochemical properties. This kind of methods are helpful to filter libraries in terms of drug-likeness or lead-likeness or to predict certain pharmacokinetic properties to help decision making (26). 2D-methods employ physicochemical proper-ties assigned to 2D-structure of compounds. Molecu-lar fingerprints are widely used to determine simi-larity between compounds regarding fragment con-nectivity independent from their spatial orientations, which is handled by 3D-methods, such as pharma-cophore modelling (Figure 2) and shape similarity (27-29). The increase in the order of dimensions causes computational burden. For example, in 3D-methods, similarity between two compounds is cal-culated by finding the best molecular volume align-ment, which is selected from possible conformations

of the compounds. This requires coordination data for each atom, which is re-calculated for each ge-ometry. However, increasing computational burden is usually the result of demands for higher precision in molecular modelling. In addition, 3D-geometry and spatial volume are valuable data in CADD con-sidering drug-target affinity. In an effort to reduce computation burden by maintaining precision, new methods were introduced in which 3D-molecular fields are reduced into 1D-descriptors or molecular volume information is compressed and described as vectors (30, 31).

4.2. Structure-Based Methods

Two things gave rise to the emergence of structure-based methods as powerful tools for CADD: advanc-es in biomolecular spectroscopic methods, such as X ray diffraction and NMR, and rapid development of computer technologies, especially processors and graphics. X ray diffractometers have become widely accessible, which triggered an avalanche of struc-tural data of biological macromolecules deposited in the web sites called protein data bank. Handling macromolecule data requires high-performing pro-cessors and graphics, which is available to individu-als, except clusters of parallel processors required in the case complex dynamics simulations. The impor-

Figure 2. Last-generation azole antifungals (A) and a pharmacophore model of the last-generation azole antifungals aligned with voriconazole (B). The model consists of three rings, two hydrophobic groups, and one H bond donor represented as brown rings, green spheres and a blue sphere with an arrow, respectively. The cut-off space for each pharmacophore is highlighted with a transparent sphere. Voriconazole is represented as gray sticks and balls.


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tance of target macromolecule structure data became apparent with the ever expanding the knowledge of ligand-receptor affinity and interactions at molecu-lar level. CADD benefits from these developments in several situations some of which are listed below (32):

Creating a structural model for a protein when there is no spectroscopic or experimental struc-tural data

Identification of possible druggable sites for a re-ceptor

Ligand-receptor interactions and how these inter-actions manipulate signal transduction

Possible effects of certain residues, co-factors, ions, and solvents on conformational changes in macromolecule structure and its gating

The effects of charge polarization in ligand-re-ceptor binding

For these issues and more, there are powerful and popular tools, such as homology modelling, molecu-lar docking, and MD simulations, which are availa-ble either free of charge or through paid licenses (32).

4.2.1. Homology Modelling

Proteins with similar amino acid sequences tend to have similar tertiary structures. Homology modelling is a method to predict three-dimensional structure of a protein when there is no experimental structural data available. This is performed by the help of ho-mologous proteins with experimental structural data. The process starts with sequence alignment of the query structure with one or more template protein(s). This alignment and the atomic coordinates of the

template protein(s) are then used to thread the struc-tural model of the query protein. The model is further optimized by loop refinement and side chain mini-mization, and subjected to various structural assess-ment methods for validation (33, 34).

Apart from creating a whole structure model, homol-ogy modelling can be used to fill in loops and miss-ing side chains, something usually encountered with the structures obtained from protein data bank. Also, in silico modifications in protein structure such as alanine scanning is possible via homology model-ling. Thus, homology modelling is a powerful tool for structure-based modelling employed in target validation, hit generation, and optimization studies (35-37).

4.2.2. Molecular Docking

Molecular docking is an in silico technique to predict the preferred binding orientation and affinity of two molecules, a ligand and a receptor, to form a stable complex. Receptor in molecular docking is usually a macromolecule such as protein, DNA, RNA, or pep-tide, which is kept rigid; while ligand is a flexible small molecule. However, techniques for docking two macromolecules to each other or making both ligand and receptor flexible are available. Molecu-lar docking predicts countless ligand-receptor com-plexes (search space) and ranks them according to a score (docking score) which is calculated by a scor-ing function (Figure 3). Docking score is a metric used to predict how much affinity a ligand is bound to a receptor with, which is usually governed by non-bonded interactions, although it is possible to model possible covalent bonds between ligand and receptor by covalent docking method (38).

Figure 3. Molecular docking.



Biological systems work by means of signal trans-duction, which is pretty much affected by the at-tributes of ligand-receptor complexes. By predicting ligand-receptor complexes it is possible to predict the outcomes of signal transduction, which makes molecular docking a precious tool for drug design. In recent years, molecular docking has become an indispensable component of virtual screening. On the other hand, it is routinely applied to study ligand-receptor interactions at atomic level to understand the importance of certain amino acids, cofactors, chelating with metals, hydrogen and halogen bonds, solvation effects, and more (Figure 4) (39).

4.2.3. MD Simulations

While molecular docking provides a picture of a biological process, it is the dynamics of this process that actually matters for its biological consequences. MD of a system is the physical movements of all its atoms, which is simulated by adding Newtonian me-chanics to the initial conditions of each atom, i.e. en-ergies and coordinates. Then, forces between atoms or particles and potential energies are calculated at each given time period to determine trajectories for atoms or molecules, which reflect the dynamic evo-lution of the system (42).

Depending on the number of the atoms of the sys-tem and the presumed time scale, MD simulations

require a great amount of data usage and time, espe-cially compared to molecular docking or other target and ligand-based methods. To overcome this com-putational burden in a reasonable amount of time, parallel processors were introduced, some of which are available to researchers via remote access. Lately, an evolutionary method called Graphics Processing Unit (GPU) acceleration has made use of high-end graphics processors to greatly reduce computation time, making researchers less dependent to costly CPU clusters (43).

In addition to target validation, MD simulations are gradually becoming a part of virtual screening cam-paigns thanks to the advances mentioned above. MD simulations are usually considered in connection with molecular docking in virtual screening, for ex-ample to determine the stability of a ligand-receptor complex, however its connected use with ligand-based methods is becoming increasingly popular (44, 45).

5. CADD: Success Stories

In contrast with what one would expect, most of the chemical entities labelled as “drug” today come from serendipitous studies such as combinatorial chemistry (46). This is partly due to a sharp decrease in the speed of new entities hitting the market ob-served past couple of decades, when CADD is most

Figure 4. Superposition of the co-crystallized ligand (oteseconazole) and its docking pose obtained by Glide (2019-4, Schrödinger, LLC, NY, USA) (40) in Candida albicans lanosterol 14α demethylase (CACYP51) active site (A) and binding interactions of oteseconazole with CACYP51 according to the crystallographic study (B) (41). The images were rendered using Maestro (2019-4, Schrödinger, LLC, NY, USA).


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expected to bear fruit. Still, examples of drugs dis-covered through CADD inspire researchers from dif-ferent backgrounds, three of which are provided as example in this review.

Dorzolamide (Merck): Marketed as an eye-drop for glaucoma, dorzolamide was developed through structure-based drug design (47). This compound was developed as a human carbonic anhydrase II (hCAII) inhibitor and tailored to fit in the specific active site of hCAII starting from a parent racemate compound (MK-927), whose enantiomers show 100-fold difference in affinity to hCAII (48, 49). X ray crystallography studies and ab initio calculations identified the conformational variations that caused the observed potency difference between the enan-tiomers. Therefore, further modifications were taken on for the S enantiomer of MK-927 using structure-based modelling and introduction of a methyl group to the 6th position yielded a more potent derivative. The 4-isobuthylamino group was replaced with eth-ylamino group to counter the reduced water solubil-ity. The resulting four enantiomers were evaluated through in vitro and crystallographic studies and the trans S,S configuration (dorzolamide) was found to have the greatest potency (50).

Zanamivir (GSK) and Oseltamivir (Gilead Sci-ences): The antiviral drug zanamivir acts through viral neuraminidase inhibition and is used against influenza infections (51). Following the structural elucidation of neuraminidase via X ray crystallog-raphy, structure-based virtual screening campaigns were conducted to find potential anti-viral inhibitors and zanamivir is the result of one of these campaigns using the software GRID (52). Zanamivir was de-signed from 2-deoxy-2,3-dehydro-N-acetylneu-raminic acid by substitution of the 4-hydroxy group with 4-guanidino. Further structure-based design to utilize an extra binding gorge on neuraminidase led to the discovery of more potent and orally available oseltamivir (47).

Aliskiren (Novartis): Aliskiren was designed as a renin inhibitor for the treatment of hypertension. It is also the first member of the class called direct renin inhibitors. Aliskiren’s design starts with the efforts to find renin inhibitors by mimicking the natural peptide substrate of the renin system (53). The first non-peptide derivative was developed by Goschke et al. (1997), which was a success compared to the previous peptide derivatives in terms of pharmacoki-

netic profile (54). With a structure-based modelling using a crystal structure of renin, the lead designed by Goschke et al. was further optimized to improve potency. Identification of an additional pocket in the renin catalytic site through the following X ray crystallography studies led to rational design of new derivatives with better affinity and selectivity to re-nin over other aspartic peptidases and further SAR studies to optimize in silico interactions with renin resulted in a derivative, which was aliskiren, with potency at sub-nanomolar concentration (55).

6. Resources for Molecular Modelling Tools

Thanks to the age of information and the growing “open source, open data” trend, it has never been easier to understand and utilize molecular modelling tools of different sorts. The internet is full of free-to-use applications or web servers as well as tutorial materials (Table 2).

Increasing popularity of molecular modelling cre-ated demands for hands-on training and workshops, which are now a common part of scientific meetings of related fields. These workshops, some of which are supported by the leading molecular modelling software companies, offer important opportunities, especially for postgraduates and postdoctoral fel-lows. In this respect, we organized the 1st Molecular Modelling Workshop in Hacettepe University Fac-ulty of Pharmacy with Hacettepe University Me-dicinal Chemistry Research, Development, and Ap-plication Center (MAGUM) on December 5-6, 2019. The workshop focused on state-of-the-art techniques in molecular modelling for CADD (56). The par-ticipants had the opportunity to know academic free modelling tools and experience hands-on training. The workshop also provided a general idea, opportu-nities, catches, and pitfalls of virtual screening.

7. Conclusions

The overwhelming developments in computer and information technologies for the past couple of dec-ades have boosted molecular modelling and its key application, CADD. As computers became an indis-pensable material of research, so did molecular mod-elling techniques in CADD at all levels, from sketch-ing a molecule to running millisecond MD simula-



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tions, for scientists of drug research. There are many examples, in which the cost and time needed for drug discovery is reduced, complex decision-making pro-cesses are eased, disease pathways and interplay of diverse molecules are understood. In addition to availability of fast-processing computers, exponen-tially increasing amount of open data in this field is encouraging to learn and utilize molecular modelling in CADD. Actually, today there are more molecular modelling tools that are free or academic free than commercial software with paid license. The ocean called “internet” is full of documentations, tutori-als, and forums to help through getting familiar with these tools and troubleshooting. Also, molecular modelling workshops, separate or as part of scientif-ic meetings, became very common and wide-spread. Thus, every scientist in drug research can and should make the best of molecular modelling.

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